Abstract is missing.
- Continual Causality: A Retrospective of the Inaugural AAAI-23 Bridge ProgramMartin Mundt, Keiland W. Cooper, Devendra Singh Dhami, Adéle Ribeiro, James Seale Smith, Alexis Bellot, Tyler L. Hayes. 1-10 [doi]
- Continual Treatment Effect Estimation: Challenges and OpportunitiesZhixuan Chu, Sheng Li 0001. 11-17 [doi]
- Prospects of Continual Causality for Industrial ApplicationsDaigo Fujiwara, Kazuki Koyama, Keisuke Kiritoshi, Tomomi Okawachi, Tomonori Izumitani, Shohei Shimizu. 18-24 [doi]
- From IID to the Independent Mechanisms assumption in continual learningOleksiy Ostapenko, Pau Rodríguez, Alexandre Lacoste, Laurent Charlin. 25-29 [doi]
- Continually Updating Neural Causal ModelsFlorian Peter Busch, Jonas Seng, Moritz Willig, Matej Zecevic. 30-37 [doi]
- Treatment Effect Estimation to Guide Model Optimization in Continual LearningJonas Seng, Florian Peter Busch, Matej Zecevic, Moritz Willig. 38-44 [doi]
- Continual Causal AbstractionsMatej Zecevic, Moritz Willig, Florian Peter Busch, Jonas Seng. 45-51 [doi]
- Causal Concept Identification in Open World EnvironmentsMoritz Willig, Matej Zecevic, Jonas Seng, Florian Peter Busch. 52-58 [doi]
- Spurious Features in Continual LearningTimothée Lesort. 59-62 [doi]
- Towards Causal Replay for Knowledge Rehearsal in Continual LearningNikhil Churamani, Jiaee Cheong, Sinan Kalkan, Hatice Gunes. 63-70 [doi]
- Never Ending Reasoning and Learning: Opportunities and ChallengesSriraam Natarajan, Kristian Kersting. 71-74 [doi]
- Modeling Uplift from Observational Time-Series in Continual ScenariosSanghyun Kim, Jungwon Choi, Namhee Kim, Jaesung Ryu, Juho Lee 0001. 75-84 [doi]
- From Continual Learning to Causal Discovery in RoboticsLuca Castri, Sariah Mghames, Nicola Bellotto. 85-91 [doi]
- Issues for Continual Learning in the Presence of Dataset BiasDonggyu Lee, Sangwon Jung, Taesup Moon. 92-99 [doi]